Trial title: How the study of atypical populations can help in building general theories of behaviour.
Ordinary opponents:
- First opponent: Associate Professor Ángel E. Tovar, School of Psychology, National Autonomous University of Mexico
- Second opponent: Associate Professor Boudour Ammar, Department of Computer Engineering and Applied Mathematics, National Engineering School of Sfax, University of Sfax, Tunisia
- Leader of the evaluation committee: Associate Professor Sigmund Eldevik, Department of Behavioural Science, OsloMet
Leader of the public defense is Head of Department Magne Arve Flaten, Department of Behavioural Science, OsloMet.
The main supervisor is Professor Anis Yazidi, the Faculty of Technology, Art and Design, OsloMet.
The co-supervisors are Professor Erik Arntzen, Faculty of Health Sciences, OsloMet and Professor Hugo L. Hammer, Faculty of Technology, Art and Design, OsloMet.
Abstract
In this thesis, two well studied subjects in behaviour analysis are computationally modeled; formation of stimulus equivalence classes, and adaptive learning. The former is addressed in Study I and Study II, while the latter is addressed in Study III and Study IV.
Background
Stimulus equivalence as a behavioural analytic approach studies cognitive skills such as memory and learning. Despite its importance in experimental studies, from a computational modelling point of view, the formation of stimulus equivalence classes has largely been under-investigated.
On the other hand, adaptive learning in a broad sense, is a tool to study several cognitive tasks including memory and remembering. An appropriate model can be used as a cognitive level finder, and as a recommendation tool to optimize the training and learning sequence of tasks.
Aims
To propose computational models that replicate formation of stimulus equivalence classes and adaptive learning. The models are supposed to be simple, flexible and interpretable in order to be suitable for analysis of human complex behaviour.
Methods
Agents endowed with Reinforcement learning, more precisely Projective Simulation and Stochastic Point Location, are used to model the interaction between experimenter and the participant through the testing/learning process.
Formation of derived relations in Study I is achieved by on demand computation during the test phase trials using likelihood reasoning. In Study II, subsequent to the training phase, an iterative diffusion process called Network Enhancement is used to form derived relations, which turns the test phase into a memory retrieval phase.
The solution to Stochastic Point Location in Study III aims to estimate the tolerable task difficulty level in an online and interactive settings. In Study IV, the appropriate task difficulty for training and learning is sought by using a target success rate that is usually defined beforehand by the experimenter using a method called Balanced Difficulty Task Finder.
Results
Proposed models for replication of equivalence relations, called Equivalence Projective Simulation (Study I) and Enhanced Equivalence Projective Simulation (Study II) could replicate a variety of settings in a matching-to-sample procedure.
The models are quite flexible and appropriate to replicate results from real experiments and simulate different scenarios before performing an empirical experiment involving human subjects. In Study III, we suggest a new method to estimate the unknown point location in the Stochastic Point Location problem domain using the mutual probability flux concept and we prove that the proposed solution outperforms the legacy solution reported in the literature.
The probability of receiving correct response from the participant is also estimated as a measure of reliability of participant's performance. In Study IV, we propose a model that is able to suggest a manageable difficulty level to a learner based on online feedback via an asymmetric adjustment technique of difficulty.
Discussion
We aimed for models that are flexible, interpretative without a need of extensive pre-training of the model. By resorting to the theory of Projective Simulation, we propose an interpretable simulator for equivalence relations that enjoys the advantage of being easy to configure.
By virtue of the Stochastic Point Location model, it is possible to eliminate the need for prior-knowledge about the participant while also avoiding complex modelling techniques. Although not pursued in this thesis, those two lines of modelling could be used in a complementary setting.
For instance, adaptive learning can be integrated in the training phase of matching-to-sample or titrated delayed matching-to-sample procedures as suggested in Study IV.
